| We search variety of things over the Internet in our daily lives,and numerous search engines are available to get us more relevant results.With the rapid technological advancement,the internet has become a major source of obtaining information.Further,the advent of the Web2.0 era has led to increased interaction between the user and the website.It has become challenging to provide information to users as per their interests.Because of copyright restrictions,most of existing research study is confronting the lack of availability of the content of candidates recommending articles.The content of such articles is not always available freely and hence leads to inadequate recommendation results.Moreover,various research studies based on recommendation user's profiles.Therefore,their recommendation needs a significant number of registered users in the system.In recent years,research work proves that Knowledge network have yielded better in generating quality recommendation results and alleviating sparsity,and cold start issues.Network embedding techniques try to learn high quality feature vectors automatically from network structures,enabling vector-based measurers of node relatedness.Keeping the strength of Network embedding techniques,the proposed a knowledge-driven Paper recommendation makes use of Heterogeneous Network Embedding Model in generating recommendation results.The novelty of this paper tries to use matapath2 vec in learning knowledge network and find research items that meet user requirements,identifying and incorporating the latent relations across research papers could play a significant role and improve the recommendation.Unlike existing approaches,the proposed method has the capability of learning low-dimensional latent representation of nodes(i.e.,research papers)in a network.We apply metapath2 vec on a knowledge network built by the ACL Anthology Network(All about NLP)and use the node relatedness to generate article recommendations. |
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